{"cells":[{"cell_type":"markdown","source":"# 天气最高温度预测任务","metadata":{}},{"cell_type":"markdown","source":"我们要完成三项任务：\n- 使用随机森林算法完成基本建模任务\n\n基本任务需要我们处理数据，观察特征，完成建模并进行可视化展示分析\n\n- 观察数据量与特征个数对结果影响\n\n在保证算法一致的前提下，加大数据个数，观察结果变换。重新考虑特征工程，引入新特征后观察结果走势。\n\n- 对随机森林算法进行调参，找到最合适的参数\n\n掌握机器学习中两种经典调参方法，对当前模型进行调节","metadata":{}},{"cell_type":"code","source":"# 数据读取\nimport pandas as pd\n\nfeatures = pd.read_csv('./datalab/62821/temps.csv')\nfeatures.head(5)","metadata":{"hideCode":false,"hidePrompt":false,"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"   year  month  day  week  temp_2  temp_1  average  actual  friend\n0  2016      1    1   Fri      45      45     45.6      45      29\n1  2016      1    2   Sat      44      45     45.7      44      61\n2  2016      1    3   Sun      45      44     45.8      41      56\n3  2016      1    4   Mon      44      41     45.9      40      53\n4  2016      1    5  Tues      41      40     46.0      44      41","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>month</th>\n      <th>day</th>\n      <th>week</th>\n      <th>temp_2</th>\n      <th>temp_1</th>\n      <th>average</th>\n      <th>actual</th>\n      <th>friend</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Fri</td>\n      <td>45</td>\n      <td>45</td>\n      <td>45.6</td>\n      <td>45</td>\n      <td>29</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>2</td>\n      <td>Sat</td>\n      <td>44</td>\n      <td>45</td>\n      <td>45.7</td>\n      <td>44</td>\n      <td>61</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>3</td>\n      <td>Sun</td>\n      <td>45</td>\n      <td>44</td>\n      <td>45.8</td>\n      <td>41</td>\n      <td>56</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>4</td>\n      <td>Mon</td>\n      <td>44</td>\n      <td>41</td>\n      <td>45.9</td>\n      <td>40</td>\n      <td>53</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>5</td>\n      <td>Tues</td>\n      <td>41</td>\n      <td>40</td>\n      <td>46.0</td>\n      <td>44</td>\n      <td>41</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"数据表中\n* year,moth,day,week分别表示的具体的时间\n* temp_2：前天的最高温度值\n* temp_1：昨天的最高温度值\n* average：在历史中，每年这一天的平均最高温度值\n* actual：这就是我们的标签值了，当天的真实最高温度\n* friend：这一列可能是凑热闹的，你的朋友猜测的可能值，咱们不管它就好了","metadata":{}},{"cell_type":"markdown","source":"# 数据大小","metadata":{}},{"cell_type":"code","source":"print('The shape of our features is:', features.shape)","metadata":{"hideCode":false,"hidePrompt":false,"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"The shape of our features is: (348, 9)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"结果显示：The shape of our features is: (348, 9)，表示我们的数据一共有348条记录，每个样本有9个特征。如果你想观察一下各个指标的统计特性，还可以用.describe()来直接展示一下：","metadata":{}},{"cell_type":"code","source":"# 统计指标\nfeatures.describe()","metadata":{"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"         year       month         day      temp_2      temp_1     average  \\\ncount   348.0  348.000000  348.000000  348.000000  348.000000  348.000000   \nmean   2016.0    6.477011   15.514368   62.511494   62.560345   59.760632   \nstd       0.0    3.498380    8.772982   11.813019   11.767406   10.527306   \nmin    2016.0    1.000000    1.000000   35.000000   35.000000   45.100000   \n25%    2016.0    3.000000    8.000000   54.000000   54.000000   49.975000   \n50%    2016.0    6.000000   15.000000   62.500000   62.500000   58.200000   \n75%    2016.0   10.000000   23.000000   71.000000   71.000000   69.025000   \nmax    2016.0   12.000000   31.000000   92.000000   92.000000   77.400000   \n\n           actual      friend  \ncount  348.000000  348.000000  \nmean    62.543103   60.034483  \nstd     11.794146   15.626179  \nmin     35.000000   28.000000  \n25%     54.000000   47.750000  \n50%     62.500000   60.000000  \n75%     71.000000   71.000000  \nmax     92.000000   95.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>month</th>\n      <th>day</th>\n      <th>temp_2</th>\n      <th>temp_1</th>\n      <th>average</th>\n      <th>actual</th>\n      <th>friend</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>348.0</td>\n      <td>348.000000</td>\n      <td>348.000000</td>\n      <td>348.000000</td>\n      <td>348.000000</td>\n      <td>348.000000</td>\n      <td>348.000000</td>\n      <td>348.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2016.0</td>\n      <td>6.477011</td>\n      <td>15.514368</td>\n      <td>62.511494</td>\n      <td>62.560345</td>\n      <td>59.760632</td>\n      <td>62.543103</td>\n      <td>60.034483</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.0</td>\n      <td>3.498380</td>\n      <td>8.772982</td>\n      <td>11.813019</td>\n      <td>11.767406</td>\n      <td>10.527306</td>\n      <td>11.794146</td>\n      <td>15.626179</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>2016.0</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>35.000000</td>\n      <td>35.000000</td>\n      <td>45.100000</td>\n      <td>35.000000</td>\n      <td>28.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2016.0</td>\n      <td>3.000000</td>\n      <td>8.000000</td>\n      <td>54.000000</td>\n      <td>54.000000</td>\n      <td>49.975000</td>\n      <td>54.000000</td>\n      <td>47.750000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2016.0</td>\n      <td>6.000000</td>\n      <td>15.000000</td>\n      <td>62.500000</td>\n      <td>62.500000</td>\n      <td>58.200000</td>\n      <td>62.500000</td>\n      <td>60.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2016.0</td>\n      <td>10.000000</td>\n      <td>23.000000</td>\n      <td>71.000000</td>\n      <td>71.000000</td>\n      <td>69.025000</td>\n      <td>71.000000</td>\n      <td>71.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2016.0</td>\n      <td>12.000000</td>\n      <td>31.000000</td>\n      <td>92.000000</td>\n      <td>92.000000</td>\n      <td>77.400000</td>\n      <td>92.000000</td>\n      <td>95.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"其中包括了各个列的数量，如果有缺失数据，数量就有所减少，这里因为并不存在缺失值，所以各个列的数量值就都是348了，均值，标准差，最大最小值等指标在这里就都显示出来了。\n对于时间数据，我们也可以进行一些转换，目的就是有些工具包在绘图或者计算的过程中，需要标准的时间格式：","metadata":{}},{"cell_type":"code","source":"# 处理时间数据\nimport datetime\n\n# 分别得到年，月，日\nyears = features['year']\nmonths = features['month']\ndays = features['day']\n\n# datetime格式\ndates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\ndates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]","metadata":{"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"dates[:5]","metadata":{"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"[datetime.datetime(2016, 1, 1, 0, 0),\n datetime.datetime(2016, 1, 2, 0, 0),\n datetime.datetime(2016, 1, 3, 0, 0),\n datetime.datetime(2016, 1, 4, 0, 0),\n datetime.datetime(2016, 1, 5, 0, 0)]"},"metadata":{}}]},{"cell_type":"markdown","source":"# 数据展示","metadata":{}},{"cell_type":"code","source":"# 准备画图\nimport matplotlib.pyplot as plt\n\n%matplotlib inline\n\n# 指定默认风格\nplt.style.use('fivethirtyeight')","metadata":{"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"接着我们设计画图的布局，这里我们需要展示4项指标，分别为最高气温的标签值，前天，昨天，朋友预测的气温最高值。既然是4个图，那不妨就2*2的规模来画吧，这样会更清晰一点，对每个图再指定好其名字和坐标轴含义就可以了：","metadata":{}},{"cell_type":"code","source":"# 设置布局\nfig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize = (10,10))\nfig.autofmt_xdate(rotation = 45)\n\n# 标签值\nax1.plot(dates, features['actual'])\nax1.set_xlabel(''); ax1.set_ylabel('Temperature'); ax1.set_title('Max Temp')\n\n# 昨天\nax2.plot(dates, features['temp_1'])\nax2.set_xlabel(''); ax2.set_ylabel('Temperature'); ax2.set_title('Previous Max Temp')\n\n# 前天\nax3.plot(dates, features['temp_2'])\nax3.set_xlabel('Date'); ax3.set_ylabel('Temperature'); ax3.set_title('Two Days Prior Max Temp')\n\n# 我的逗逼朋友\nax4.plot(dates, features['friend'])\nax4.set_xlabel('Date'); ax4.set_ylabel('Temperature'); ax4.set_title('Friend Estimate')\n\nplt.tight_layout(pad=2)","metadata":{"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x720 with 4 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"各项指标看起来都还算正常，由于是国外的天气数据所以跟咱们的统计标准有些区别。接下来就要考虑数据预处理问题了，原始数据中在week列中并不是一些数值特征，而是表示周几的字符串，这些计算机可不认识，需要我们来转换一下：","metadata":{}},{"cell_type":"markdown","source":"# 数据预处理\n","metadata":{}},{"cell_type":"markdown","source":"### One-Hot Encoding","metadata":{}},{"cell_type":"markdown","source":"原始数据：\n\n| week |\n|------|\n| Mon  |\n| Tue  |\n| Wed  |\n| Thu  |\n| Fri  |\n\n编码转换后:\n\n| Mon | Tue | Wed | Thu | Fri |\n|-----|-----|-----|-----|-----|\n| 1   | 0   | 0   | 0   | 0   |\n| 0   | 1   | 0   | 0   | 0   |\n| 0   | 0   | 1   | 0   | 0   |\n| 0   | 0   | 0   | 1   | 0   |\n| 0   | 0   | 0   | 0   | 1   |","metadata":{}},{"cell_type":"code","source":"# 独热编码\nfeatures = pd.get_dummies(features)\nfeatures.head(5)","metadata":{"hideCode":false,"hidePrompt":false,"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"   year  month  day  temp_2  temp_1  average  actual  friend  week_Fri  \\\n0  2016      1    1      45      45     45.6      45      29         1   \n1  2016      1    2      44      45     45.7      44      61         0   \n2  2016      1    3      45      44     45.8      41      56         0   \n3  2016      1    4      44      41     45.9      40      53         0   \n4  2016      1    5      41      40     46.0      44      41         0   \n\n   week_Mon  week_Sat  week_Sun  week_Thurs  week_Tues  week_Wed  \n0         0         0         0           0          0         0  \n1         0         1         0           0          0         0  \n2         0         0         1           0          0         0  \n3         1         0         0           0          0         0  \n4         0         0         0           0          1         0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>year</th>\n      <th>month</th>\n      <th>day</th>\n      <th>temp_2</th>\n      <th>temp_1</th>\n      <th>average</th>\n      <th>actual</th>\n      <th>friend</th>\n      <th>week_Fri</th>\n      <th>week_Mon</th>\n      <th>week_Sat</th>\n      <th>week_Sun</th>\n      <th>week_Thurs</th>\n      <th>week_Tues</th>\n      <th>week_Wed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>1</td>\n      <td>45</td>\n      <td>45</td>\n      <td>45.6</td>\n      <td>45</td>\n      <td>29</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>2</td>\n      <td>44</td>\n      <td>45</td>\n      <td>45.7</td>\n      <td>44</td>\n      <td>61</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>3</td>\n      <td>45</td>\n      <td>44</td>\n      <td>45.8</td>\n      <td>41</td>\n      <td>56</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>4</td>\n      <td>44</td>\n      <td>41</td>\n      <td>45.9</td>\n      <td>40</td>\n      <td>53</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2016</td>\n      <td>1</td>\n      <td>5</td>\n      <td>41</td>\n      <td>40</td>\n      <td>46.0</td>\n      <td>44</td>\n      <td>41</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"这样就完成了数据集中属性值的预处理工作，默认会把所有属性值都转换成独热编码的格式，并且还帮我们自动添加了后缀看起来更清晰了，这里我们其实也可以按照自己的方式来设置编码特征的名字的，如果大家遇到了一个不太熟悉的函数，想看一下其中的细节，有一个更直接的方法就是在notebook当中直接调help工具来看一下它的API文档，下面返回的就是其细节介绍，不光有各个参数说明，还有一些小例子，建议大家在使用的过程中一定要养成多练多查的习惯，查找解决问题的方法也是一个很重要的技能：","metadata":{}},{"cell_type":"code","source":"print (help(pd.get_dummies))","metadata":{"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"Help on function get_dummies in module pandas.core.reshape.reshape:\n\nget_dummies(data, prefix=None, prefix_sep='_', dummy_na=False, columns=None, sparse=False, drop_first=False)\n    Convert categorical variable into dummy/indicator variables\n    \n    Parameters\n    ----------\n    data : array-like, Series, or DataFrame\n    prefix : string, list of strings, or dict of strings, default None\n        String to append DataFrame column names\n        Pass a list with length equal to the number of columns\n        when calling get_dummies on a DataFrame. Alternatively, `prefix`\n        can be a dictionary mapping column names to prefixes.\n    prefix_sep : string, default '_'\n        If appending prefix, separator/delimiter to use. Or pass a\n        list or dictionary as with `prefix.`\n    dummy_na : bool, default False\n        Add a column to indicate NaNs, if False NaNs are ignored.\n    columns : list-like, default None\n        Column names in the DataFrame to be encoded.\n        If `columns` is None then all the columns with\n        `object` or `category` dtype will be converted.\n    sparse : bool, default False\n        Whether the dummy columns should be sparse or not.  Returns\n        SparseDataFrame if `data` is a Series or if all columns are included.\n        Otherwise returns a DataFrame with some SparseBlocks.\n    drop_first : bool, default False\n        Whether to get k-1 dummies out of k categorical levels by removing the\n        first level.\n    \n        .. versionadded:: 0.18.0\n    Returns\n    -------\n    dummies : DataFrame or SparseDataFrame\n    \n    Examples\n    --------\n    >>> import pandas as pd\n    >>> s = pd.Series(list('abca'))\n    \n    >>> pd.get_dummies(s)\n       a  b  c\n    0  1  0  0\n    1  0  1  0\n    2  0  0  1\n    3  1  0  0\n    \n    >>> s1 = ['a', 'b', np.nan]\n    \n    >>> pd.get_dummies(s1)\n       a  b\n    0  1  0\n    1  0  1\n    2  0  0\n    \n    >>> pd.get_dummies(s1, dummy_na=True)\n       a  b  NaN\n    0  1  0    0\n    1  0  1    0\n    2  0  0    1\n    \n    >>> df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],\n    ...                    'C': [1, 2, 3]})\n    \n    >>> pd.get_dummies(df, prefix=['col1', 'col2'])\n       C  col1_a  col1_b  col2_a  col2_b  col2_c\n    0  1       1       0       0       1       0\n    1  2       0       1       1       0       0\n    2  3       1       0       0       0       1\n    \n    >>> pd.get_dummies(pd.Series(list('abcaa')))\n       a  b  c\n    0  1  0  0\n    1  0  1  0\n    2  0  0  1\n    3  1  0  0\n    4  1  0  0\n    \n    >>> pd.get_dummies(pd.Series(list('abcaa')), drop_first=True)\n       b  c\n    0  0  0\n    1  1  0\n    2  0  1\n    3  0  0\n    4  0  0\n    \n    See Also\n    --------\n    Series.str.get_dummies\n\nNone\n","output_type":"stream"}]},{"cell_type":"code","source":"print('Shape of features after one-hot encoding:', features.shape)","metadata":{"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Shape of features after one-hot encoding: (348, 15)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### 标签与数据格式转换","metadata":{}},{"cell_type":"code","source":"# 数据与标签\nimport numpy as np\n\n# 标签\nlabels = np.array(features['actual'])\n\n# 在特征中去掉标签\nfeatures= features.drop('actual', axis = 1)\n\n# 名字单独保存一下，以备后患\nfeature_list = list(features.columns)\n\n# 转换成合适的格式\nfeatures = np.array(features)","metadata":{"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"markdown","source":"### 训练集与测试集","metadata":{}},{"cell_type":"code","source":"# 数据集切分\nfrom sklearn.model_selection import train_test_split\n\ntrain_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.25,\n                                                                           random_state = 42)","metadata":{"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"print('训练集特征:', train_features.shape)\nprint('训练集标签:', train_labels.shape)\nprint('测试集特征:', test_features.shape)\nprint('测试集标签:', test_labels.shape)","metadata":{"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"训练集特征: (261, 14)\n训练集标签: (261,)\n测试集特征: (87, 14)\n测试集标签: (87,)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# 建立一个基础的随机森林模型\n万事俱备，我们可以来建立随机森林模型啦，首先导入工具包，先建立1000个树试试吧，其他参数先用默认值，之后我们会再深入到调参任务中：","metadata":{}},{"cell_type":"code","source":"# 导入算法\nfrom sklearn.ensemble import RandomForestRegressor\n\n# 建模\nrf = RandomForestRegressor(n_estimators= 1000, random_state=42)\n\n# 训练\nrf.fit(train_features, train_labels)","metadata":{"hideCode":false,"hidePrompt":false,"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n           max_features='auto', max_leaf_nodes=None,\n           min_impurity_decrease=0.0, min_impurity_split=None,\n           min_samples_leaf=1, min_samples_split=2,\n           min_weight_fraction_leaf=0.0, n_estimators=1000, n_jobs=1,\n           oob_score=False, random_state=42, verbose=0, warm_start=False)"},"metadata":{}}]},{"cell_type":"markdown","source":"由于数据样本量还是非常小的，所以很快就可以得到结果了，这里我们先用MAPE指标来进行评估，也就是平均绝对百分误差，其实对于回归任务，评估方法还是比较多，给大家列出来几种，很简单就可以实现出来，也可以选择其他指标来进行评估：","metadata":{}},{"cell_type":"markdown","source":"# 测试","metadata":{}},{"cell_type":"code","source":"# 预测结果\npredictions = rf.predict(test_features)\n\n# 计算误差\nerrors = abs(predictions - test_labels)\n\n# mean absolute percentage error (MAPE)\nmape = 100 * (errors / test_labels)\n\nprint ('MAPE:',np.mean(mape))","metadata":{"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"MAPE: 6.00942279601\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# MAPE指标","metadata":{}},{"cell_type":"markdown","source":"# 可视化展示树","metadata":{}},{"cell_type":"code","source":"# 导入所需工具包\nfrom sklearn.tree import export_graphviz\nimport pydot #pip install pydot\n\n# 拿到其中的一棵树\ntree = rf.estimators_[5]\n\n# 导出成dot文件\nexport_graphviz(tree, out_file = 'tree.dot', feature_names = feature_list, rounded = True, precision = 1)\n\n# 绘图\n(graph, ) = pydot.graph_from_dot_file('tree.dot')\n\n# 展示\ngraph.write_png('tree.png'); ","metadata":{"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"markdown","source":"![Decision Tree](tree.png)","metadata":{}},{"cell_type":"code","source":"print('The depth of this tree is:', tree.tree_.max_depth)","metadata":{"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":"The depth of this tree is: 15\n","output_type":"stream"}]},{"cell_type":"markdown","source":"还是小一点吧。。。","metadata":{}},{"cell_type":"code","source":"# 限制一下树模型\nrf_small = RandomForestRegressor(n_estimators=10, max_depth = 3, random_state=42)\nrf_small.fit(train_features, train_labels)\n\n# 提取一颗树\ntree_small = rf_small.estimators_[5]\n\n# 保存\nexport_graphviz(tree_small, out_file = 'small_tree.dot', feature_names = feature_list, rounded = True, precision = 1)\n\n(graph, ) = pydot.graph_from_dot_file('small_tree.dot')\n\ngraph.write_png('small_tree.png');","metadata":{"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"markdown","source":"![Small Decision Tree](images/small_tree.PNG)","metadata":{}},{"cell_type":"markdown","source":"### Annotated Version of Tree","metadata":{}},{"cell_type":"markdown","source":"![Annotated Decision Tree](images/small_tree_annotated.PNG)","metadata":{}},{"cell_type":"markdown","source":"## 特征重要性","metadata":{}},{"cell_type":"code","source":"# 得到特征重要性\nimportances = list(rf.feature_importances_)\n\n# 转换格式\nfeature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]\n\n# 排序\nfeature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n\n# 对应进行打印\n[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances]","metadata":{"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"Variable: temp_1               Importance: 0.7\nVariable: average              Importance: 0.19\nVariable: day                  Importance: 0.03\nVariable: temp_2               Importance: 0.02\nVariable: friend               Importance: 0.02\nVariable: month                Importance: 0.01\nVariable: year                 Importance: 0.0\nVariable: week_Fri             Importance: 0.0\nVariable: week_Mon             Importance: 0.0\nVariable: week_Sat             Importance: 0.0\nVariable: week_Sun             Importance: 0.0\nVariable: week_Thurs           Importance: 0.0\nVariable: week_Tues            Importance: 0.0\nVariable: week_Wed             Importance: 0.0\n","output_type":"stream"},{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"[None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None,\n None]"},"metadata":{}}]},{"cell_type":"markdown","source":"### 用最重要的特征再来试试","metadata":{}},{"cell_type":"code","source":"# 选择最重要的那两个特征来试一试\nrf_most_important = RandomForestRegressor(n_estimators= 1000, random_state=42)\n\n# 拿到这俩特征\nimportant_indices = [feature_list.index('temp_1'), feature_list.index('average')]\ntrain_important = train_features[:, important_indices]\ntest_important = test_features[:, important_indices]\n\n# 重新训练模型\nrf_most_important.fit(train_important, train_labels)\n\n# 预测结果\npredictions = rf_most_important.predict(test_important)\n\nerrors = abs(predictions - test_labels)\n\n# 评估结果\n\nmape = np.mean(100 * (errors / test_labels))\n\nprint('mape:', mape)","metadata":{"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"mape: 6.2035840065\n","output_type":"stream"}]},{"cell_type":"code","source":"# 转换成list格式\nx_values = list(range(len(importances)))\n\n# 绘图\nplt.bar(x_values, importances, orientation = 'vertical')\n\n# x轴名字\nplt.xticks(x_values, feature_list, rotation='vertical')\n\n# 图名\nplt.ylabel('Importance'); plt.xlabel('Variable'); plt.title('Variable Importances'); ","metadata":{"trusted":true},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"### 预测值与真实值之间的差异","metadata":{}},{"cell_type":"code","source":"# 日期数据\nmonths = features[:, feature_list.index('month')]\ndays = features[:, feature_list.index('day')]\nyears = features[:, feature_list.index('year')]\n\n# 转换日期格式\ndates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\ndates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in dates]\n\n# 创建一个表格来存日期和其对应的标签数值\ntrue_data = pd.DataFrame(data = {'date': dates, 'actual': labels})\n\n# 同理，再创建一个来存日期和其对应的模型预测值\nmonths = test_features[:, feature_list.index('month')]\ndays = test_features[:, feature_list.index('day')]\nyears = test_features[:, feature_list.index('year')]\n\ntest_dates = [str(int(year)) + '-' + str(int(month)) + '-' + str(int(day)) for year, month, day in zip(years, months, days)]\n\ntest_dates = [datetime.datetime.strptime(date, '%Y-%m-%d') for date in test_dates]\n\npredictions_data = pd.DataFrame(data = {'date': test_dates, 'prediction': predictions}) ","metadata":{"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"# 真实值\nplt.plot(true_data['date'], true_data['actual'], 'b-', label = 'actual')\n\n# 预测值\nplt.plot(predictions_data['date'], predictions_data['prediction'], 'ro', label = 'prediction')\nplt.xticks(rotation = '60'); \nplt.legend()\n\n# 图名\nplt.xlabel('Date'); plt.ylabel('Maximum Temperature (F)'); plt.title('Actual and Predicted Values');\n","metadata":{"trusted":true},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":"看起来还可以，这个走势我们的模型已经基本能够掌握了，接下来我们要再深入到数据中了，考虑几个问题：\n1.如果可以利用的数据量增大，会对结果产生什么影响呢？\n2.加入新的特征会改进模型效果吗？此时的时间效率又会怎样？","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}],"metadata":{"hide_code_all_hidden":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.8","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":2}